Recently, in the field of ITS (Intelligent Transport System), attention has been drawn to object detection techniques in which a pedestrian, a vehicle, a traffic sign, etc. in an image taken by a car-mounted camera are detected by using IT (Information Technology).
Although detection of an object in an image taken outdoors is not easy due to changes in a lighting condition, occlusion (overlap between objects in the image), etc., it has been studied enthusiastically all over the world because it is efficient for reducing traffic accidents.
As a method for detecting an object in an image, a method employing an HOG (Histograms of Oriented Gradients) feature is known. The HOG feature can represent a shape of the object existing in the image. The HOG feature is derived from intensity information of each pixel of the image, and is represented as a histogram obtained based on an orientation and a magnitude of an intensity gradient at a local area (cell) in the image.
Non Patent Literature 1 discloses a method employing the HOG feature and an SVM (Support Vector Machine). In this method, the HOG feature of a cell (block) having a certain size is calculated successively while the cell is being moved in the image to detect whether a human exists in the image or not.
In addition, as methods for detecting objects employing the HOG feature, Non Patent Literature 2 discloses a method employing a Joint feature representing co-occurrence between a plurality of the HOG features, and Non Patent Literature 3 discloses a method in which a plurality of the HOG features are computed by changing sizes of blocks.